



"""simple docstring"""
import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_      						=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_      						=			{
    '''microsoft/wavlm-base''': '''https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json''',
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}




class       snake_case_    (    __snake_case					):
							__lowerCAmelCase            =       """wavlm"""






							def __init__( self      ,  a_=3_2      ,  a_=7_6_8      ,  a_=1_2      ,  a_=1_2      ,  a_=3_0_7_2      ,  a_="gelu"      ,  a_=0.1      ,  a_=0.1      ,  a_=0.1      ,  a_=0.0      ,  a_=0.1      ,  a_=0.1      ,  a_=0.02      ,  a_=1e-5      ,  a_="group"      ,  a_="gelu"      ,  a_=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)      ,  a_=(5, 2, 2, 2, 2, 2, 2)      ,  a_=(1_0, 3, 3, 3, 3, 2, 2)      ,  a_=False      ,  a_=1_2_8      ,  a_=1_6      ,  a_=3_2_0      ,  a_=8_0_0      ,  a_=False      ,  a_=True      ,  a_=0.05      ,  a_=1_0      ,  a_=2      ,  a_=0.0      ,  a_=1_0      ,  a_=3_2_0      ,  a_=2      ,  a_=0.1      ,  a_=1_0_0      ,  a_=2_5_6      ,  a_=2_5_6      ,  a_=0.1      ,  a_="mean"      ,  a_=False      ,  a_=False      ,  a_=2_5_6      ,  a_=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)      ,  a_=(5, 3, 3, 1, 1)      ,  a_=(1, 2, 3, 1, 1)      ,  a_=5_1_2      ,  a_=8_0      ,  a_=0      ,  a_=1      ,  a_=2      ,  a_=False      ,  a_=3      ,  a_=2      ,  a_=3      ,  a_=None      ,  **a_      ,  ):
									super().__init__(**__SCREAMING_SNAKE_CASE      ,  pad_token_id=__SCREAMING_SNAKE_CASE      ,  bos_token_id=__SCREAMING_SNAKE_CASE      ,  eos_token_id=__SCREAMING_SNAKE_CASE   )
									a_  :   List[Any]			    =  hidden_size
									a_  :   List[str]			    =  feat_extract_norm
									a_  :   str			    =  feat_extract_activation
									a_  :   List[Any]			    =  list(__SCREAMING_SNAKE_CASE   )
									a_  :   List[str]			    =  list(__SCREAMING_SNAKE_CASE   )
									a_  :   Any			    =  list(__SCREAMING_SNAKE_CASE   )
									a_  :   Union[str, Any]			    =  conv_bias
									a_  :   str			    =  num_buckets
									a_  :   Tuple			    =  max_bucket_distance
									a_  :   int			    =  num_conv_pos_embeddings
									a_  :   Union[str, Any]			    =  num_conv_pos_embedding_groups
									a_  :   List[str]			    =  len(self.conv_dim   )
									a_  :   Dict			    =  num_hidden_layers
									a_  :   Any			    =  intermediate_size
									a_  :   Optional[int]			    =  hidden_act
									a_  :   Dict			    =  num_attention_heads
									a_  :   Tuple			    =  hidden_dropout
									a_  :   Dict			    =  attention_dropout
									a_  :   Any			    =  activation_dropout
									a_  :   Union[str, Any]			    =  feat_proj_dropout
									a_  :   List[Any]			    =  final_dropout
									a_  :   List[Any]			    =  layerdrop
									a_  :   Any			    =  layer_norm_eps
									a_  :   List[str]			    =  initializer_range
									a_  :   List[Any]			    =  num_ctc_classes
									a_  :   Any			    =  vocab_size
									a_  :   int			    =  do_stable_layer_norm
									a_  :   int			    =  use_weighted_layer_sum
									a_  :   Optional[int]			    =  classifier_proj_size

									if (
									    (len(self.conv_stride   ) != self.num_feat_extract_layers)
									    or (len(self.conv_kernel   ) != self.num_feat_extract_layers)
									    or (len(self.conv_dim   ) != self.num_feat_extract_layers)
									):
											raise ValueError(
											    "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
											    " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
											    F""" {len(self.conv_dim   )}`, `len(config.conv_stride) = {len(self.conv_stride   )}`,"""
											    F""" `len(config.conv_kernel) = {len(self.conv_kernel   )}`."""   )

									# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
									a_  :   Optional[Any]			    =  apply_spec_augment
									a_  :   int			    =  mask_time_prob
									a_  :   List[str]			    =  mask_time_length
									a_  :   List[Any]			    =  mask_time_min_masks
									a_  :   Optional[int]			    =  mask_feature_prob
									a_  :   Union[str, Any]			    =  mask_feature_length

									# parameters for pretraining with codevector quantized representations
									a_  :   List[str]			    =  num_codevectors_per_group
									a_  :   List[Any]			    =  num_codevector_groups
									a_  :   List[str]			    =  contrastive_logits_temperature
									a_  :   Optional[int]			    =  num_negatives
									a_  :   Dict			    =  codevector_dim
									a_  :   Dict			    =  proj_codevector_dim
									a_  :   Any			    =  diversity_loss_weight

									# ctc loss
									a_  :   Any			    =  ctc_loss_reduction
									a_  :   List[Any]			    =  ctc_zero_infinity

									# adapter
									a_  :   Tuple			    =  add_adapter
									a_  :   Tuple			    =  adapter_kernel_size
									a_  :   Dict			    =  adapter_stride
									a_  :   Optional[Any]			    =  num_adapter_layers
									a_  :   Optional[Any]			    =  output_hidden_size or hidden_size

									# SequenceClassification-specific parameter. Feel free to ignore for other classes.
									a_  :   Any			    =  classifier_proj_size

									# XVector-specific parameters. Feel free to ignore for other classes.
									a_  :   str			    =  list(__SCREAMING_SNAKE_CASE   )
									a_  :   Optional[int]			    =  list(__SCREAMING_SNAKE_CASE   )
									a_  :   Optional[Any]			    =  list(__SCREAMING_SNAKE_CASE   )
									a_  :   Union[str, Any]			    =  xvector_output_dim






							@property
							def snake_case_	( self   ):
									return functools.reduce(operator.mul      ,  self.conv_stride      ,  1   )







"""simple docstring"""





import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
_SCREAMING_SNAKE_CASE   :							int          =		pd.read_csv(
    '''https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'''
    '''position_salaries.csv'''
)
_SCREAMING_SNAKE_CASE   :							List[Any]          =		dataset.iloc[:, 1:2].values
_SCREAMING_SNAKE_CASE   :							str          =		dataset.iloc[:, 2].values


_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE   :							Dict          =		train_test_split(X, y, test_size=0.2, random_state=0)


_SCREAMING_SNAKE_CASE   :							Optional[int]          =		PolynomialFeatures(degree=4)
_SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		poly_reg.fit_transform(X)
_SCREAMING_SNAKE_CASE   :							List[str]          =		LinearRegression()
pol_reg.fit(X_poly, y)







def 	lowerCamelCase__			(				) ->							List[str]:
  plt.scatter(_lowerCamelCase						,							_lowerCamelCase						,							color='red'					)
  plt.plot(_lowerCamelCase						,							pol_reg.predict(poly_reg.fit_transform(_lowerCamelCase					)					)						,							color='blue'					)
  plt.title('Truth or Bluff (Linear Regression)'					)
  plt.xlabel('Position level'					)
  plt.ylabel('Salary'					)
  plt.show()


if __name__ == "__main__":
  viz_polymonial()

  # Predicting a new result with Polymonial Regression
  pol_reg.predict(poly_reg.fit_transform([[5.5]]))
  # output should be 132148.43750003




